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Right now it's possible to use tf.keras.callbacks.ModelCheckpoint to save (keras) model weights during training under NengoDL, and then use sim.keras_model.load_weights(...) afterwards. However it might be nice to have our own version of this that uses the native save_params and load_params directly.
I briefly looked at the ModelCheckpointcode and after a quick scan it looks like it should be pretty reasonable to subclass.
The text was updated successfully, but these errors were encountered:
Right now it's possible to use
tf.keras.callbacks.ModelCheckpoint
to save (keras) model weights during training under NengoDL, and then usesim.keras_model.load_weights(...)
afterwards. However it might be nice to have our own version of this that uses the nativesave_params
andload_params
directly.I briefly looked at the
ModelCheckpoint
code and after a quick scan it looks like it should be pretty reasonable to subclass.The text was updated successfully, but these errors were encountered: